PHLAC - Image Classification Using Local Posterior Probabilities.

Image Classification Using
Local Posterior Probabilities

PHLAC features: We presents a novel image representation method for generic object recognition by using higher-order local autocorrelations on posterior probability images. The proposed method is an extension of the bag-of-features approach to posterior probability images. The standard bag-of-features approach is approximately thought of as a method that classifies an image to a category whose sum of posterior probabilities on a posterior probability image is maximum. However, by using local autocorrelations of posterior probability images, the proposed method extracts richer information than the standard bag-of-features. Experimental results reveal that the proposed method exhibits higher classification performances than the standard bag-of-features method.

Extensions: We presents scene classification methods using spatial relationship between local posterior probabilities of each category. Recently, the authors proposed the probability higher-order local autocorrelations (PHLAC) feature. This method uses autocorrelations of local posterior probabilities to capture spatial distributions of local posterior probabilities of a category. Although PHLAC achieves good recognition accuracies for scene classification, we can improve the performance further by using crosscorrelation between categories. We extend PHLAC features to crosscorrelations of posterior probabilities of other categories. Also, we introduce the subtraction operator for describing another spatial relationship of local posterior probabilities, and present vertical/horizontal mask patterns for the spatial layout of auto/crosscorrelations. Since the combination of category index is large, we compress the proposed features by two-dimensional principal component analysis. We confirmed the effectiveness of the proposed methods using Scene-15 dataset, and our method exhibited competitive performances to recent methods without using spatial grid informations and even using linear classifiers.

Publications: We have publised following papers:

Image Classification Using Probability Higher-order Local Auto-Correlations
T. Matsukawa and T. Kurita.
The Nineth Asian Conference on Computer Vision (ACCV),
Part III, LNCS 5996, Xi'an, September 2009. [pdf][poster]

SCENE CLASSIFICATION USING SPATIAL RELATOINSHIP OF LOCAL POSTERIOR PROBABILITIES
T. Matsukawa and T. Kurita.
International Conference on Vision Theory and Applications (VISAPP),
Angers, May 2010. [pdf]

Image representation for generic object recognition using higher-order local autocorrelation features on posterior probability images
T. Matsukawa and T. Kurita.
Pattern Recognition (Submitted),
Elsevier.

Contact: Tetsu Matsukawa, Takio Kurita

Image Classification Using Local Posterior Probabilities

PHLAC features: We presents a novel image representation method for generic object recognition by using higher-order local autocorrelations on posterior probability images. The proposed method is an extension of the bag-of-features approach to posterior probability images. The standard bag-of-features approach is approximately thought of as a method that classifies an image to a category whose sum of posterior probabilities on a posterior probability image is maximum. However, by using local autocorrelations of posterior probability images, the proposed method extracts richer information than the standard bag-of-features. Experimental results reveal that the proposed method exhibits higher classification performances than the standard bag-of-features method. Extensions: We presents scene classification methods using spatial relationship between local posterior probabilities of each category. Recently, the authors proposed the probability higher-order local autocorrelations (PHLAC) feature. This method uses autocorrelations of local posterior probabilities to capture spatial distributions of local posterior probabilities of a category. Although PHLAC achieves good recognition accuracies for scene classification, we can improve the performance further by using crosscorrelation between categories. We extend PHLAC features to crosscorrelations of posterior probabilities of other categories. Also, we introduce the subtraction operator for describing another spatial relationship of local posterior probabilities, and present vertical/horizontal mask patterns for the spatial layout of auto/crosscorrelations. Since the combination of category index is large, we compress the proposed features by two-dimensional principal component analysis. We confirmed the effectiveness of the proposed methods using Scene-15 dataset, and our method exhibited competitive performances to recent methods without using spatial grid informations and even using linear classifiers.
Publications:	We have publised following papers: Image Classification Using Probability Higher-order Local Auto-Correlations T. Matsukawa and T. Kurita. The Nineth Asian Conference on Computer Vision (ACCV), Part III, LNCS 5996, Xi'an, September 2009. [pdf][poster] SCENE CLASSIFICATION USING SPATIAL RELATOINSHIP OF LOCAL POSTERIOR PROBABILITIES T. Matsukawa and T. Kurita. International Conference on Vision Theory and Applications (VISAPP), Angers, May 2010. [pdf] Image representation for generic object recognition using higher-order local autocorrelation features on posterior probability images T. Matsukawa and T. Kurita. Pattern Recognition (Submitted), Elsevier.
Contact:	Tetsu Matsukawa, Takio Kurita